(00:00:00) – How batch size affects token cost and speed
(00:32:09) – How MoE models are laid out across a GPU racks
(00:47:12) – How pipeline parallelism moves model layers across racks
(01:03:37) – Why Ilya said, “As we now know, pipelining is not wise.”
(01:18:59) – Because of RL, models may be 100x over-trained beyond Chinchilla-optimal
(01:33:02) – Deducing long context memory costs from API pricing
(02:04:02) – Convergent evolution between neural nets and cryptography

My answer to: https://www.reddit.com/r/Clojure/comments/pcwypb/us_engineers_love_to_say_the_right_tool_for_the/ which asked to know when and at what is Clojure "the right tool for the job"?

My take is that in general, the right tool for the job actually doesn't matter that much when it comes to programming language.

There are only a few cases where the options of tools that can do a sufficiently good job at the task become limited.

That's why they are called: General-purpose programming languages, because they can be used generally for most use cases without issues.

Let's look at some of the dimensions that make a difference and what I think of Clojure for them:

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Ejecutar el comando enable

Switch> enable

• IaaS指提供系统（可以自己选）或者储存空间之类的硬件，软件要自己手动装。PaaS提供语言环境和框架（可以自己选）。SaaS只能使用开发好的软件（卖软件本身，如税务会计、表格文字处理）。BaaS一般类似于非关系数据库，但各家不通用
• 云服务的特点：零前期成本 & 按需付费 & 弹性（类似于租，可随时多加、退掉；但没有残值）、高可用（放在机房中，不同AZ间水电隔离）

如果你想补充内容，建议优先给 free-for-dev 提PR，还能混个高星repo的contributor，没必要加到本列表里。
If you want to make improvements, I would recommend you contributing to free-for-dev rather than this list.

You ask one AI a question, you get one answer. That answer

A full-stack recipe management app where you can browse, search, add, edit, and view detailed recipes.
Built with a React frontend, Express/Node backend, MongoDB database, and JWT-based authentication.

Live Demo

1. Run the command:

   python pixiv_auth.py login

   This will open the browser with Pixiv login page.

Orange unfortunately blocks manually updating the DNS servers for my own network, since I wanted to have my home server running dnsmasq, I went ahead and found a way around this limitation. It does, however, get reset to original values when the router is restarted.

Login to the admin page of the router and navigate to Advanced Settings > DNS.

Inspect the DNS field and go to the console tab. We want to run the following commands inside the advanced_network_dns.htm frame context.

Update the IPs as needed.